The history of Philosoph - from the view of data science

Part 0: Load needed libraries and the dataset

library(rvest)
library(tibble)
library(tidyverse)
library(ggplot2)
library(qdap)
library(sentimentr)
library(gplots)
library(hrbrthemes)
library(tm)
library(syuzhet)
library(factoextra)
library(beeswarm)
library(scales)
library(RColorBrewer)
library(RANN)
library(topicmodels)
library(wordcloud)
library(tidytext)
library(knitr)
library(DT)
library(plotly)

philosophy_data <- read_csv("philosophy_data.csv",col_types = cols())

The School of Athens [4]

Part 1: Data Exploration

The dataset used in this project contains over 360,000 sentences from over 50 texts spanning 13 major schools of the history of philosophy. The represented schools are: Plato, Aristotle, Empiricism, Rationalism, Analytic, Continental, Phenomenology, German Idealism, Communism, Capitalism, Stoicism, Nietzsche, and Feminism. Besides, there are 36 represented authors from each school, such as Epictetus from Stoicism or Marx from Communism.

dim(philosophy_data)

## [1] 360808     11

datatable(head(philosophy_data,n=500), 
          options = list(pageLength = 3, scrollX = TRUE, scrollCollapse = TRUE, searching = FALSE))

unique(philosophy_data$school)

##  [1] "plato"           "aristotle"       "empiricism"      "rationalism"    
##  [5] "analytic"        "continental"     "phenomenology"   "german_idealism"
##  [9] "communism"       "capitalism"      "stoicism"        "nietzsche"      
## [13] "feminism"

unique(philosophy_data$author)

##  [1] "Plato"           "Aristotle"       "Locke"           "Hume"           
##  [5] "Berkeley"        "Spinoza"         "Leibniz"         "Descartes"      
##  [9] "Malebranche"     "Russell"         "Moore"           "Wittgenstein"   
## [13] "Lewis"           "Quine"           "Popper"          "Kripke"         
## [17] "Foucault"        "Derrida"         "Deleuze"         "Merleau-Ponty"  
## [21] "Husserl"         "Heidegger"       "Kant"            "Fichte"         
## [25] "Hegel"           "Marx"            "Lenin"           "Smith"          
## [29] "Ricardo"         "Keynes"          "Epictetus"       "Marcus Aurelius"
## [33] "Nietzsche"       "Wollstonecraft"  "Beauvoir"        "Davis"

Based on the above data exploration, I noticed that this is a rich dataset of philosophical sentences spanning from 350 BC to modern 1985 AD. Given such a long time span, I became interested in whether the social change had any impact on the development of the history of philosophy.
Therefore, I made an interactive graph of the dataset in chronological order. As can be seen from the figure, I can roughly divide the period over 2300 years into three stages: (1) the first stage from 350 BC to 125 AD; (2) the second stage from 125 AD to 1637 AD; (3) the third stage from 1637 AD to 1985 AD.

timeline <- philosophy_data%>%
  group_by(original_publication_date, author) %>%
  summarise(count=n(), .groups = 'drop')

p1 <- timeline %>%
  ggplot(aes(x=original_publication_date, y=count)) +
  geom_area(fill="#69b3a2", alpha=0.5) +
  geom_line(color="#69b3a2") +
  labs(x = "Year(350BC~1985)",
       y = "Number of sentence for each author published") +
  theme_ipsum()
p1 <- ggplotly(p1)
p1

In the following pages, I will do Wordcloud and Sentiment analysis based on these three time periods, and try to answer the questions I raised from a statistical point of view.

Part 2: Data Analysis

First Stage: 350 BC ~ 125 AD

timeline1 <- philosophy_data%>%
  filter(original_publication_date<125)
timeline1_info <- timeline1%>%
  group_by(school, author) %>%
  distinct(school, author, title, original_publication_date)%>%
  arrange(original_publication_date)
kable(timeline1_info)

title	author	school	original_publication_date
Plato - Complete Works	Plato	plato	-350
Aristotle - Complete Works	Aristotle	aristotle	-320

sentence.list=NULL
cols = c('title', 'author', 'school', 'sentence_str', 'original_publication_date')
for(i in 1:nrow(timeline1)){
  sentences=str_remove_all(timeline1$tokenized_txt[i], "[\\[\\],']")
  if(length(sentences)>0){
      emotions=get_nrc_sentiment(sentences)
      word.count=word_count(sentences)
        # colnames(emotions)=paste0("emo.", colnames(emotions))
        # in case the word counts are zeros?
      emotions=emotions/(word.count+0.01)
      sentence.list=rbind(sentence.list, 
                          cbind(timeline1[i,cols],
                                sentences=as.character(sentences), 
                                word.count,
                                emotions)
        )
      }
}
write.csv(sentence.list,"../output/sentiment_1.csv", row.names = TRUE, col.names = TRUE)

sentiment_1 <- read_csv("sentiment_1.csv", col_types = cols())
dim(sentiment_1)

## [1] 87145    18

sentiment_1 <- sentiment_1%>%
  filter(!is.na(word.count))%>%
  filter((anger!=0)&(anticipation!=0)&(disgust!=0)&(fear!=0)
         &(joy!=0)&(sadness!=0)&(surprise!=0)&(trust!=0)) %>%
  mutate(neutral = if_else((negative==0)&(positive==0),1,0)) %>%
  mutate(main_emotion=apply(.[c((9:16),19)], 1, function(x) names(x)[which.max(x)])) %>%
  mutate(main_attitude=apply(.[c((17:19),20)], 1, function(x) names(x)[which.max(x)])) %>%
  select(-sentences)
dim(sentiment_1)

## [1] 2217   20

head(sentiment_1)

sentiment_1%>%
  group_by(main_emotion, school) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=main_emotion, y = index, fill=school)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  labs(x='Emotions',
       y='',
       title='Sentiment analysis for emotions among time period 1')

sentiment_1%>%
  filter(main_emotion=='anger') %>%
  arrange(desc(anger)) %>%
  select(author,sentence_str,main_emotion,anger)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_1%>%
  filter(main_emotion=='trust') %>%
  arrange(desc(trust)) %>%
  select(author,sentence_str,main_emotion,trust)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_1%>%
  group_by(main_attitude, school) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=school, y = index, fill=main_attitude)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  labs(x='School',
       y='',
       title='Sentiment analysis for attitudes among time period 1')

sentiment_1%>%
  filter(main_attitude=='positive') %>%
  arrange(desc(positive)) %>%
  select(author,sentence_str,main_attitude,positive)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_1%>%
  filter(main_attitude=='negative') %>%
  arrange(desc(negative)) %>%
  select(author,sentence_str,main_attitude,negative)%>%
  group_by(author) %>%
  slice_head(n=2)

mywords <- c('one', 'will', 'also')
sent1 <- Corpus(VectorSource(timeline1$sentence_lowered))
sent1 <- tm_map(sent1, stripWhitespace)
sent1 <- tm_map(sent1, removeNumbers)
sent1 <- tm_map(sent1, removeWords, stopwords("english"))
sent1 <- tm_map(sent1, removeWords, character(0))
sent1 <- tm_map(sent1, removePunctuation)
tdm1 <- TermDocumentMatrix(sent1)
tdm1.tidy = tidy(tdm1)
tdm1.overall=summarise(group_by(tdm1.tidy, term), sum(count))
tdm1.overall <- tdm1.overall %>%
  rename(n = 'sum(count)') %>%
  mutate(perc = n/sum(n)) %>%
  arrange(desc(n))
kable(tdm1.overall[1:20,])

term	n	perc
one	14282	0.0151597
will	8479	0.0090001
things	7438	0.0078951
must	6754	0.0071691
man	6087	0.0064611
also	5510	0.0058486
good	4890	0.0051905
now	4750	0.0050419
say	4719	0.0050090
thing	4541	0.0048201
said	4116	0.0043689
can	4091	0.0043424
way	3929	0.0041705
like	3790	0.0040229
just	3540	0.0037575
time	3504	0.0037193
two	3347	0.0035527
something	3317	0.0035208
another	3222	0.0034200
may	3141	0.0033340

wordcloud(tdm1.overall$term, tdm1.overall$n,
          scale=c(5,0.5),
          max.words=100,
          min.freq=10,
          random.order=FALSE,
          rot.per=0.3,
          random.color=FALSE,
          colors=brewer.pal(9,"Dark2"))

Second Stage: 125 AD ~ 1637 AD

timeline2 <- philosophy_data%>%
  filter(original_publication_date>=125 & original_publication_date<1637)
timeline2_info <- timeline2%>%
  group_by(school, author) %>%
  distinct(school, author, title, original_publication_date)%>%
  arrange(original_publication_date)
kable(timeline2_info)

title	author	school	original_publication_date
Enchiridion	Epictetus	stoicism	125
Meditations	Marcus Aurelius	stoicism	170

sentence.list=NULL
cols = c('title', 'author', 'school', 'sentence_str', 'original_publication_date')
for(i in 1:nrow(timeline2)){
  sentences=str_remove_all(timeline2$tokenized_txt[i], "[\\[\\],']")
  if(length(sentences)>0){
      emotions=get_nrc_sentiment(sentences)
      word.count=word_count(sentences)
        # colnames(emotions)=paste0("emo.", colnames(emotions))
        # in case the word counts are zeros?
      emotions=emotions/(word.count+0.01)
      sentence.list=rbind(sentence.list, 
                          cbind(timeline2[i,cols],
                                sentences=as.character(sentences), 
                                word.count,
                                emotions)
        )
      }
}
write.csv(sentence.list,"../output/sentiment_2.csv", row.names = TRUE, col.names = TRUE)

sentiment_2 <- read_csv("sentiment_2.csv", col_types = cols())
dim(sentiment_2)

## [1] 2535   18

sentiment_2 <- sentiment_2%>%
  filter(!is.na(word.count))%>%
  filter((anger!=0)&(anticipation!=0)&(disgust!=0)&(fear!=0)
         &(joy!=0)&(sadness!=0)&(surprise!=0)&(trust!=0)) %>%
  mutate(neutral = if_else((negative==0)&(positive==0),1,0)) %>%
  mutate(main_emotion=apply(.[c((9:16),19)], 1, function(x) names(x)[which.max(x)])) %>%
  mutate(main_attitude=apply(.[c((17:19),20)], 1, function(x) names(x)[which.max(x)])) %>%
  select(-sentences)
dim(sentiment_2)

## [1] 92 20

head(sentiment_2)

sentiment_2%>%
  group_by(main_emotion, author) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=main_emotion, y = index, fill=author)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  labs(x='Emotions',
       y='',
       title='Sentiment analysis for emotions among time period 2')

sentiment_2%>%
  filter(main_emotion=='anger') %>%
  arrange(desc(anger)) %>%
  select(author,sentence_str,main_emotion,anger)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_2%>%
  filter(main_emotion=='fear') %>%
  arrange(desc(fear)) %>%
  select(author,sentence_str,main_emotion,fear)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_2%>%
  group_by(main_attitude, author) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=author, y = index, fill=main_attitude)) +
  geom_bar(position="fill", stat="identity") + 
  coord_flip() +
  labs(x='Author',
       y='Percentage',
       title='Sentiment analysis for attitudes among time period 2')

sentiment_2%>%
  filter(main_attitude=='positive') %>%
  arrange(desc(positive)) %>%
  select(author,sentence_str,main_attitude,positive)%>%
  group_by(author) %>%
  slice_head(n=2)

sentiment_2%>%
  filter(main_attitude=='negative') %>%
  arrange(desc(negative)) %>%
  select(author,sentence_str,main_attitude,negative)%>%
  group_by(author) %>%
  slice_head(n=2)

sent2 <- Corpus(VectorSource(timeline2$sentence_lowered))
sent2 <- tm_map(sent2, stripWhitespace)
sent2 <- tm_map(sent2, removeNumbers)
sent2 <- tm_map(sent2, removeWords, stopwords("english"))
sent2 <- tm_map(sent2, removeWords, character(0))
sent2 <- tm_map(sent2, removePunctuation)
tdm2 <- TermDocumentMatrix(sent2)
tdm2.tidy = tidy(tdm2)
tdm2.overall=summarise(group_by(tdm2.tidy, term), sum(count))
tdm2.overall <- tdm2.overall %>%
  rename(n = 'sum(count)') %>%
  mutate(perc = n/sum(n)) %>%
  arrange(desc(n))
kable(tdm2.overall[1:20,])

term	n	perc
thou	798	0.0272830
things	568	0.0194195
unto	436	0.0149065
thy	338	0.0115560
one	313	0.0107012
thee	306	0.0104619
man	302	0.0103251
nature	268	0.0091627
either	251	0.0085815
doth	247	0.0084447
will	246	0.0084105
good	195	0.0066669
thyself	193	0.0065985
must	188	0.0064276
whatsoever	171	0.0058464
also	165	0.0056412
now	157	0.0053677
world	155	0.0052993
upon	153	0.0052309
can	152	0.0051968

wordcloud(tdm2.overall$term, tdm2.overall$n,
          scale=c(5,0.5),
          max.words=100,
          min.freq=10,
          random.order=FALSE,
          rot.per=0.3,
          random.color=FALSE,
          colors=brewer.pal(9,"Dark2"))

Third Stage: 1637 AD ~ 1985 AD

timeline3 <- philosophy_data%>%
  filter(original_publication_date>=1637)
timeline3_info <- timeline3%>%
  group_by(school, author) %>%
  distinct(school, author, title,original_publication_date)%>%
  arrange(original_publication_date)
kable(timeline3_info)

title	author	school	original_publication_date
Discourse On Method	Descartes	rationalism	1637
Meditations On First Philosophy	Descartes	rationalism	1641
The Search After Truth	Malebranche	rationalism	1674
Ethics	Spinoza	rationalism	1677
On The Improvement Of Understanding	Spinoza	rationalism	1677
Second Treatise On Government	Locke	empiricism	1689
Essay Concerning Human Understanding	Locke	empiricism	1689
A Treatise Concerning The Principles Of Human Knowledge	Berkeley	empiricism	1710
Theodicy	Leibniz	rationalism	1710
Three Dialogues	Berkeley	empiricism	1713
A Treatise Of Human Nature	Hume	empiricism	1739
The Wealth Of Nations	Smith	capitalism	1776
Dialogues Concerning Natural Religion	Hume	empiricism	1779
Critique Of Pure Reason	Kant	german_idealism	1781
Critique Of Practical Reason	Kant	german_idealism	1788
Critique Of Judgement	Kant	german_idealism	1790
Vindication Of The Rights Of Woman	Wollstonecraft	feminism	1792
The System Of Ethics	Fichte	german_idealism	1798
The Phenomenology Of Spirit	Hegel	german_idealism	1807
Science Of Logic	Hegel	german_idealism	1817
On The Principles Of Political Economy And Taxation	Ricardo	capitalism	1817
Elements Of The Philosophy Of Right	Hegel	german_idealism	1820
The Communist Manifesto	Marx	communism	1848
Essential Works Of Lenin	Lenin	communism	1862
Capital	Marx	communism	1883
Beyond Good And Evil	Nietzsche	nietzsche	1886
Thus Spake Zarathustra	Nietzsche	nietzsche	1887
The Antichrist	Nietzsche	nietzsche	1888
Ecce Homo	Nietzsche	nietzsche	1888
Twilight Of The Idols	Nietzsche	nietzsche	1888
The Idea Of Phenomenology	Husserl	phenomenology	1907
Philosophical Studies	Moore	analytic	1910
The Problems Of Philosophy	Russell	analytic	1912
The Analysis Of Mind	Russell	analytic	1921
Tractatus Logico-Philosophicus	Wittgenstein	analytic	1921
Being And Time	Heidegger	phenomenology	1927
The Crisis Of The European Sciences And Phenomenology	Husserl	phenomenology	1936
A General Theory Of Employment, Interest, And Money	Keynes	capitalism	1936
The Phenomenology Of Perception	Merleau-Ponty	phenomenology	1945
The Second Sex	Beauvoir	feminism	1949
Quintessence	Quine	analytic	1950
On Certainty	Wittgenstein	analytic	1950
Off The Beaten Track	Heidegger	phenomenology	1950
Philosophical Investigations	Wittgenstein	analytic	1953
The Logic Of Scientific Discovery	Popper	analytic	1959
History Of Madness	Foucault	continental	1961
The Birth Of The Clinic	Foucault	continental	1963
The Order Of Things	Foucault	continental	1966
Writing And Difference	Derrida	continental	1967
Difference And Repetition	Deleuze	continental	1968
Naming And Necessity	Kripke	analytic	1972
Anti-Oedipus	Deleuze	continental	1972
Philosophical Troubles	Kripke	analytic	1975
Women, Race, And Class	Davis	feminism	1981
Lewis - Papers	Lewis	analytic	1985

timeline3 %>%
  group_by(school) %>%
  summarize(article_per_school = n_distinct(title)) %>%
  ggplot(aes(x=school, y=article_per_school)) +
  geom_bar(stat='identity', fill='blue', alpha=0.6) +
  labs(y='Number of articles', x='School') +
  theme(axis.text.x = element_text(angle = 90))

sentence.list=NULL
cols = c('title', 'author', 'school', 'sentence_str', 'original_publication_date')
for(i in 1:nrow(timeline3)){
  sentences=str_remove_all(timeline3$tokenized_txt[i], "[\\[\\],']")
  if(length(sentences)>0){
      emotions=get_nrc_sentiment(sentences)
      word.count=word_count(sentences)
        # colnames(emotions)=paste0("emo.", colnames(emotions))
        # in case the word counts are zeros?
      emotions=emotions/(word.count+0.01)
      sentence.list=rbind(sentence.list, 
                          cbind(timeline3[i,cols],
                                sentences=as.character(sentences), 
                                word.count,
                                emotions)
        )
      }
}
write.csv(sentence.list,"../output/sentiment_3.csv", row.names = TRUE, col.names = TRUE)

sentiment_3 <- read_csv("sentiment_3.csv", col_types = cols())
dim(sentiment_3)

## [1] 271128     18

sentiment_3 <- sentiment_3%>%
  filter(!is.na(word.count))%>%
  filter((anger!=0)&(anticipation!=0)&(disgust!=0)&(fear!=0)
         &(joy!=0)&(sadness!=0)&(surprise!=0)&(trust!=0)) %>%
  mutate(neutral = if_else((negative==0)&(positive==0),1,0)) %>%
  mutate(main_emotion=apply(.[c((9:16),19)], 1, function(x) names(x)[which.max(x)])) %>%
  mutate(main_attitude=apply(.[c((17:19),20)], 1, function(x) names(x)[which.max(x)])) %>%
  select(-sentences)
dim(sentiment_3)

## [1] 6717   20

head(sentiment_3)

sentiment_3%>%
  group_by(main_emotion, school) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=school, y = index, fill=main_emotion)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  facet_wrap(~main_emotion) +
  ggtitle('Sentiment analysis for emotions among time period 3')

sentiment_3%>%
  filter(main_emotion=='anger') %>%
  arrange(desc(anger)) %>%
  select(school,sentence_str,main_emotion,anger)%>%
  group_by(school) %>%
  slice_head(n=2)

sentiment_3%>%
  filter(main_emotion=='trust') %>%
  arrange(desc(trust)) %>%
  select(school,sentence_str,main_emotion,trust)%>%
  group_by(school) %>%
  slice_head(n=2)

sentiment_3%>%
  group_by(main_attitude, school) %>%
  summarize_all(n_distinct) %>%
  ggplot(aes(x=school, y = index, fill=main_attitude)) +
  geom_bar(position="fill", stat="identity") + 
  coord_flip() +
  labs(x='School',
       y='Percentage',
       title='Sentiment analysis for attitudes among time period 3')

sentiment_3%>%
  filter(main_attitude=='positive') %>%
  arrange(desc(positive)) %>%
  select(school,sentence_str,main_attitude,positive)%>%
  group_by(school) %>%
  slice_head(n=2)

sentiment_3%>%
  filter(main_attitude=='negative') %>%
  arrange(desc(negative)) %>%
  select(school,sentence_str,main_attitude,negative)%>%
  group_by(school) %>%
  slice_head(n=2)

sent3 <- Corpus(VectorSource(timeline3$sentence_lowered))
sent3 <- tm_map(sent3, stripWhitespace)
sent3 <- tm_map(sent3, removeNumbers)
sent3 <- tm_map(sent3, removeWords, stopwords("english"))
sent3 <- tm_map(sent3, removeWords, character(0))
sent3 <- tm_map(sent3, removePunctuation)
tdm3 <- TermDocumentMatrix(sent3)
tdm3.tidy = tidy(tdm3)
tdm3.overall=summarise(group_by(tdm3.tidy, term), sum(count))
tdm3.overall <- tdm3.overall %>%
  rename(n = 'sum(count)') %>%
  mutate(perc = n/sum(n)) %>%
  arrange(desc(n))
kable(tdm3.overall[1:20,])

term	n	perc
one	31944	0.0093754
can	22441	0.0065863
will	19156	0.0056222
must	14195	0.0041662
may	13008	0.0038178
time	11626	0.0034122
even	11614	0.0034087
world	11394	0.0033441
also	11105	0.0032593
first	10714	0.0031445
nature	9670	0.0028381
things	9556	0.0028046
therefore	9389	0.0027556
without	9342	0.0027418
reason	9162	0.0026890
two	9109	0.0026735
thus	8988	0.0026379
way	8863	0.0026013
man	8671	0.0025449
now	8133	0.0023870

wordcloud(tdm3.overall$term, tdm3.overall$n,
          scale=c(5,0.5),
          max.words=100,
          min.freq=10,
          random.order=FALSE,
          rot.per=0.3,
          random.color=FALSE,
          colors=brewer.pal(9,"Dark2"))

Part 3: Conclusion

First stage: 350BC ~ 125AD

During this time period, our dataset contains the complete work of two foundational schools and their corresponding most influential author: Plato and Aristotle.

Plato’s philosophical thought is a huge system, the core of which is the “ideology theory”. In his view, the world is divided into two parts: the natural world in the senses and the supernatural world in the ideas. Because the perceived world is always changing, people’s understanding of it varies from time to time, from place to place, from person to person, so the perceived world is unreal.[1]

Aristotle’s philosophical epistemology begins with a critique of Plato’s theory of ideas. In his “Metaphysics”, he believes that the natural world is an objective and real existence, and people’s cognition comes from their sense of the objective world. Without sense, there is no knowledge.[1]

Although their philosophical thoughts are not completely similar, their status as pioneers and founders in the history of philosophy is unquestionable. It can be seen from the above sentiment analysis that the dominated emotions in the sentences of Plato and Aristotle are anger, anticipation and trust. There is no obvious positive or negative difference between the attitudes in their sentences, but both are mostly positive.

Besides, based on the wordcloud for this time period, we can find some interesting relatively high-frequency words in their sentences, such as reason, nature, animals and knowledge, which is inline with their philosophy.

Second stage: 125AD ~ 1637AD

During this time period, our dataset contains one school - Stoicism - and its corresponding two authors: Epictetus and Marcus Aurelius.

The first thing catches my attention is that although this is a time span over 1500 years, there are only two authors who were active in the 2nd century AD including in this dataset. I have two possible explanations for this: first, the lack of philosophical data during this period is due to the limitation of the dataset; second, the philosophical thought during this period in history was relatively inactive due to other factors, such as the rise of mainstream religious thought.

From the sentiment analysis of Epictetus and Marcus Aurelius, I find that anger and anticipation are the two main emotions. Significantly different from Plato and Aristotle in the first stage, in the attitude analysis of Epictetus and Marcus Aurelius, negative attitudes account for a large proportion (both greater than 60%). From the wordcloud of the second stage, the common high-frequency words that attract attention are life, world, universe and soul.

Third stage: 1637 AD ~ 1985 AD

The third time period is the closest one to us. Therefore, I expect the results to be the most understandable and familiar before the analysis.

The dataset at this stage contains 10 schools and their corresponding 32 authors. We can find that although this period is only over 300 years old, it has the most schools and authors. It seems that in this time period, mankind’s philosophical thinking has in flourished.

Combined with my knowledge of history, I think the reasons for the active development of philosophy at this stage are due to (1) the Renaissance movement, which peaked in the 16th century, and (2) the Industrial Revolution, which began in the 18th century. The Renaissance liberated the mind and liberated people from the theocracy of the Middle Ages, while the labor liberation brought about by the Industrial Revolution promoted the improvement of people’s living standards.[3] The improvement of these two aspects has led to the vigorous development of modern human civilization, which also includes philosophy.

From the sentiment analysis at this stage, I observe that anger, anticipation, fear and trust are the main emotions. Different from the second stage, the attitude analysis of the third stage pointed out that positive dominated the attitude sentiment in all 10 schools (all exceeded 50%). In the wordcloud at this stage, the interesting high-frequency words I observed are self, existence, labor and power.

In conclusion, through the above analysis, we should be able to see that behind the longriver of history of philosophy, there is the impact of social changes in general.

Part 4: Reference

[1] Bakalis, Nikolaos (2005). Handbook of Greek Philosophy: From Thales to the Stoics Analysis and Fragments, Trafford Publishing ISBN 978-1-4120-4843-9.
[2] Bury, J. B.; Meiggs, Russell (1956). A history of Greece to the death of Alexander the Great. London: Macmillan. pp. 397, 540.
[3] Pincus, Steve (2009). 1688: The First Modern Revolution (2011 ed.). Yale University Press. ISBN 978-0-300-17143-3.
[4] Raphael (1509-1511). The School of Athens.